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RFM Analysis
Python notebook using data from multiple data sources · 820 views · 4mo ago·pandas, e-commerce services
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21
Nagesh Singh Chauhan
Simge Erek
Sreshta Putchala
Stephen Adewoyin
Mehmet A.
Ritesh Yadav
Pinar Dogan
Umerkk12
Irem KARAKAYA
Ozan GÜNER
Arif Eker
Halenur Bulgu
Haonan Dong
hangyi2333
Onur Akcakaya
Input
133.96 MB
folder

Data Sources

      arrow_drop_down

      Online Retail II UCI

      Online Retail II UCI
          calendar_view_week

          online_retail_II.csv

          online_retail_II.csv (90.46 MB)
      arrow_drop_down

      UCI Online Retail II Data Set

      UCI Online Retail II Data Set
          arrow_right
          calendar_view_week

          online_retail_II.xlsx

          online_retail_II.xlsx (43.51 MB)
Online Retail II UCI
A real online retail transaction data set of two years.

Last Updated: 2 years ago

About this Dataset

Context

This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

Content

Attribute Information:

InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation.
StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product.
Description: Product (item) name. Nominal.
Quantity: The quantities of each product (item) per transaction. Numeric.
InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated.
UnitPrice: Unit price. Numeric. Product price per unit in sterling (£).
CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer.
Country: Country name. Nominal. The name of the country where a customer resides.

Acknowledgements

Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link].
Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link].
Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019.
Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019.
Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018.

Execution Info
Succeeded
True
Exit Code
0
Used All Space
False
Environment
Container Image (Dockerfile)
Run Time
159.7 seconds
Timeout Exceeded
False
Output Size
0
Accelerator
None
TimeLine #Log Message
11.1s1/kaggle/input/uci-online-retail-ii-data-set/online_retail_II.xlsx
11.1s2/kaggle/input/online-retail-ii-uci/online_retail_II.csv
13.7s3Collecting xlrd
14.0s4 Downloading xlrd-2.0.1-py2.py3-none-any.whl (96 kB)
14.1s5[?25l  |███▍ | 10 kB 9.6 MB/s eta 0:00:01  |██████▉ | 20 kB 2.7 MB/s eta 0:00:01  |██████████▏ | 30 kB 1.9 MB/s eta 0:00:01  |█████████████▋ | 40 kB 1.8 MB/s eta 0:00:01  |█████████████████ | 51 kB 919 kB/s eta 0:00:01  |████████████████████▍ | 61 kB 1.0 MB/s eta 0:00:01  |███████████████████████▊ | 71 kB 1.1 MB/s eta 0:00:01  |███████████████████████████▏ | 81 kB 1.2 MB/s eta 0:00:01  |██████████████████████████████▌ | 92 kB 1.2 MB/s eta 0:00:01  |████████████████████████████████| 96 kB 1.0 MB/s
19.3s6[?25hInstalling collected packages: xlrd
19.6s7Successfully installed xlrd-2.0.1
22.1s8Collecting openpyxl
22.4s9 Downloading openpyxl-3.0.6-py2.py3-none-any.whl (242 kB)
22.6s10[?25l  |█▍ | 10 kB 7.9 MB/s eta 0:00:01  |██▊ | 20 kB 2.4 MB/s eta 0:00:01  |████ | 30 kB 1.7 MB/s eta 0:00:01  |█████▍ | 40 kB 1.6 MB/s eta 0:00:01  |██████▊ | 51 kB 808 kB/s eta 0:00:01  |████████ | 61 kB 898 kB/s eta 0:00:01  |█████████▍ | 71 kB 977 kB/s eta 0:00:01  |██████████▉ | 81 kB 1.0 MB/s eta 0:00:01  |████████████▏ | 92 kB 1.1 MB/s eta 0:00:01  |█████████████▌ | 102 kB 1.1 MB/s eta 0:00:01  |██████████████▉ | 112 kB 1.1 MB/s eta 0:00:01  |████████████████▏ | 122 kB 1.1 MB/s eta 0:00:01  |█████████████████▌ | 133 kB 1.1 MB/s eta 0:00:01  |██████████████████▉ | 143 kB 1.1 MB/s eta 0:00:01  |████████████████████▎ | 153 kB 1.1 MB/s eta 0:00:01  |█████████████████████▋ | 163 kB 1.1 MB/s eta 0:00:01  |███████████████████████ | 174 kB 1.1 MB/s eta 0:00:01  |████████████████████████▎ | 184 kB 1.1 MB/s eta 0:00:01  |█████████████████████████▋ | 194 kB 1.1 MB/s eta 0:00:01  |███████████████████████████ | 204 kB 1.1 MB/s eta 0:00:01  |████████████████████████████▎ | 215 kB 1.1 MB/s eta 0:00:01  |█████████████████████████████▊ | 225 kB 1.1 MB/s eta 0:00:01  |███████████████████████████████ | 235 kB 1.1 MB/s eta 0:00:01  |████████████████████████████████| 242 kB 1.1 MB/s
...
143.9s35dtypes: datetime64[ns](1), float64(2), int64(1), object(4)
143.9s36memory usage: 33.1+ MB
156.7s37/opt/conda/lib/python3.7/site-packages/traitlets/traitlets.py:2561: FutureWarning: --Exporter.preprocessors=["remove_papermill_header.RemovePapermillHeader"] for containers is deprecated in traitlets 5.0. You can pass `--Exporter.preprocessors item` ... multiple times to add items to a list.
156.7s38 FutureWarning,
156.7s39[NbConvertApp] Converting notebook __notebook__.ipynb to notebook
157.2s40[NbConvertApp] Writing 90563 bytes to __notebook__.ipynb
158.6s41/opt/conda/lib/python3.7/site-packages/traitlets/traitlets.py:2561: FutureWarning: --Exporter.preprocessors=["nbconvert.preprocessors.ExtractOutputPreprocessor"] for containers is deprecated in traitlets 5.0. You can pass `--Exporter.preprocessors item` ... multiple times to add items to a list.
158.6s42 FutureWarning,
158.6s43[NbConvertApp] Converting notebook __notebook__.ipynb to html
159.6s44[NbConvertApp] Writing 340638 bytes to __results__.html
159.6s45
159.6s46...
Complete. Exited with code 0.

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Irem KARAKAYA • Posted on Version 8 of 8 • 4 months ago • Options •

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Good Work, Congrats and thank you !!

Onur AkcakayaTopic Author • Posted on Version 8 of 8 • 4 months ago • Options •

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Thank you for your comment @remkarakaya

Ozan GÜNER • Posted on Version 6 of 8 • 4 months ago • Options •

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Great work👍 Thanks for sharing..

Onur AkcakayaTopic Author • Posted on Version 8 of 8 • 4 months ago • Options •

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Thanks for reviewing it @oktayozangner

Enes Topaçoğlu • Posted on Version 4 of 8 • 4 months ago • Options •

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Great work. Thanks for your sharing 👍

Onur AkcakayaTopic Author • Posted on Version 6 of 8 • 4 months ago • Options •

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Thank you for your review @enestopacoglu

Pinar Dogan • Posted on Version 8 of 8 • 4 months ago • Options •

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Very good notebook about an important topic, RFM. Thank you for sharing!

Onur AkcakayaTopic Author • Posted on Version 8 of 8 • 4 months ago • Options •

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Thank you @pinardogan. I am glad you you like it!!

Mehmet A. • Posted on Version 8 of 8 • 4 months ago • Options •

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what a nice and good kernel congrats @onurakcakaya 🖖

Onur AkcakayaTopic Author • Posted on Version 8 of 8 • 4 months ago • Options •

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@mathchi It is good to know that you like it !!!!

Ritesh Yadav • Posted on Version 6 of 8 • 4 months ago • Options •

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Great work, Very Helpful +upvoted!! :) Please Checkout this notebook feedbacks are very much appreciated!! :)
https://www.kaggle.com/ritesh2000/bert-all-in-one

Nagesh Singh Chauhan • Posted on Version 4 of 8 • 4 months ago • Options •

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Informative Notebook, nicely explained. Thank you for sharing with us. Upvoted.

I have created a notebook on "hugging face transformer basic usage"
Here, https://www.kaggle.com/nageshsingh/huggingface-transformer-basic-usage

Umerkk12 • Posted on Version 8 of 8 • 4 months ago • Options •

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Nice work. Check out my work too https://www.kaggle.com/umerkk12/titanic-dataset-eda-0-78

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